Computer Science 287/487
Instructor: Daniel Štefankovič
Class: Monday, Wednesday 2:00pm-3:15pm, ONLINE (Zoom).
Instructor office hours, ONLINE (Zoom): Tuesday 9pm-10pm, Thursday 8am-9am.
Textbooks: there is no required textbook; see each section for the recommended reading (mostly online; I will also supply handouts).
Other resources/recommended reading:
- [MR95] Randomized Algorithms, R. Motwani, P. Raghavan, 1995.
- [H02] Finite Markov chains and algorithmic applications, O. Haggstrom, 2002.
- [J03] Counting, sampling and integrating: algorithms and complexity, M. Jerrum, 2003.
- [H16] Perfect Simulation, M. Huber, 2016.
- [MU17] Probability and Computing: Randomized Algorithms and Probabilistic Analysis (2nd ed), M. Mitzenmacher, E. Upfal, 2017.
TOPIC 1: Introduction (1 class)
Introduction to counting and sampling.
Exact counting, approximate counting (FPRAS), approximate sampling.
Basic graphical models (Ising model, Potts model, colorings, hard-core model).
Survey of results and open problems.
TOPIC 2: Exact counting (2-3 classes)
Spanning trees (determinant).
Planar matchings (pfaffian).
TOPIC 3: Approximate counting (2-3 classes)
Monte Carlo (Markov, Chebyshev, Chernoff, squared coefficient of variation).
Rejection sampling, counting DNF assignments.
TOPIC 4: Markov chain basics (2 classes)
Markov chain, ergodicity. Stationary distribution.
Ergodicity. Total variation distance. Mixing time.
Reversibility. Detailed balance condition.
TOPIC 5: Coupling (3-4 classes)
Colorings. Linear extensions. Spanning trees.
Censoring. Coupling for Ising model.
TOPIC 6: Perfect sampling (2-3 classes)
Coupling from the past.
LLL based perfect sampling. Network reliability.
TOPIC 7: Canonical paths (4-5 classes)
Conductance, canonical paths, lower and upper bounds on mixing time.
High temperature expansion for Ising model.
TOPIC 8: Tight results (2 classes)
Correlation decay technique for the hard-core model.
Tight hardness result for the hard-core model.
TOPIC 9: Recent results (4-5 classes)
Sampling Bases of a Matroid.
- N. Anari, S. Oveis Gharan, C. Vinzant. Log-Concave Polynomials I: Entropy and a Deterministic Approximation Algorithm for Counting Bases of Matroids.
- N. Anari, K. Liu, S. Oveis Gharan, C. Vinzant. Log-Concave Polynomials II: High-Dimensional Walks and an FPRAS for Counting Bases of a Matroid.
- N. Anari, K. Liu, S. Oveis Gharan, C. Vinzant. Log-Concave Polynomials III: Mason's Ultra-Log-Concavity
Conjecture for Independent Sets of Matroids.
- N. Anari, K. Liu, S. Oveis Gharan, C. Vinzant. Log-Concave Polynomials IV: Exchange Properties, Tight Mixing Times, and Faster Sampling of Spanning Trees.
- M. Cryan, H. Guo, G. Mousa. Modified log-Sobolev inequalities for strongly log-concave distributions.
- V. Alev, L. Lau. Improved Analysis of Higher Order Random Walks and Applications.
- N. Anari, K. Liu, S. Oveis Gharan. Spectral Independence in High-Dimensional Expanders and Applications to the Hardcore Model.
- Z. Chen, K. Liu, E. Vigoda. Rapid Mixing of Glauber Dynamics up to Uniqueness via Contraction.
- Z. Chen, A. Galanis, D. Štefankovič, E. Vigoda. Rapid Mixing for Colorings via Spectral Independence.
- W. Feng, H. Guo, Y. Yin, C.Zhang. Rapid mixing from spectral independence beyond the Boolean domain.
- H. Guo, M. Jerrum. Random Cluster Dynamics for the Ising Model is Rapidly Mixing.
The grade for the course is based on the homework (theoretical and applied; there will be 5-7 problem sets) and a small project; there are no exams.
- Theoretical homework should be typeset (LaTeX preferred) and emailed to the instructor by the deadline (specific for each assignment). You may work with other people on the theoretical homework, but you must each write up your solutions separately (without any written aid). If you work with other people, indicate who you worked with on your solution.
- No late homework will be accepted without instructor's permission (a permission must be requested at least 24 hours before the due date).
This course follows
The University Policy on Academic Honesty